Creating Pathways for Data Proficiency in Undergraduate Students

对精通统计学和最近的数据科学的劳动力的需求继续增加。在全国高等教育中，统计学课程经常在各种学科中教授，如数学，生物学，经济学，工程学，政治学，心理学，商业和教育学。这些课程往往有特定学科的动机和内容重点，以及特定学科的例子和练习，学生有望从中学习统计学的一般概念。学生往往没有很好地通过采取这些课程，因为他们中的许多人不符合其他部门提供的后续统计或数据科学课程的先决条件要求。此外，这些课程不一定符合专业组织的一般建议和统计实践标准。美国统计协会（阿萨）的《统计学本科课程指南》建议，课程应强调使用真实的数据、使用技术和交流思想。该提案的一个主要目标是研究如何创建学生能够熟练掌握数据的途径。该项目汇集了来自LMU的数学，生物学，经济学，工程学，政治学，心理学，商业和教育的代表，将致力于建立入门统计课程之间的凝聚力，重点是阿萨报告中的三项建议。该项目将研究这三个主题如何在不同学科的统计课程中表现出来的异同，并研究教师和学生在教学和学习中对这三个主题的参与。数据来源将包括教师调查，课堂观察，课堂材料和学生工作的集合，以及多学科教师话语讨论跨学科统计的相似性和差异。通过交叉验证数据源，该项目将解决研究问题：（1）跨学科的入门和高级统计课程的共同学习目标，成果和经验是什么，以及这些如何与阿萨指南保持一致？以及（2）教师教学和学生工作在多大程度上展示了对真实的数据、技术和跨学科交流这三个主题的参与？

The need for a workforce proficient in statistics, and more recently, data science, continues to increase. In higher education nationally, statistics courses are frequently taught in a variety of disciplines, such as mathematics, biology, economics, engineering, political science, psychology, business, and education. These courses tend to have discipline-specific motivations and content emphases, as well as discipline-specific examples and exercises from which students are expected to learn general concepts in statistics. Students often are not well served by taking these courses since many of them do not fulfill the prerequisite requirements for subsequent statistics or data science courses offered by other departments. Furthermore, these courses are not necessarily aligned with general recommendations from professional organizations and standards of statistical practice. The Curriculum Guidelines for Undergraduate Programs in Statistics, from the American Statistical Association (ASA), recommends that courses should emphasize working with real data, working with technology, and communicating ideas. A main goal of this proposal is to research how to create pathways in which students can become data proficient. Bringing together representatives from mathematics, biology, economics, engineering, political science, psychology, business, and education at LMU, this project will work toward building cohesion among the introductory statistics courses with an emphasis on those three recommendations from the ASA report.Focusing on the needed institutional change to create pathways, the project will examine the similarities and differences of how the three themes are manifested in statistics courses across disciplines, and study faculty and student engagement with the three themes in their teaching and learning. Data sources will include faculty surveys, classroom observations, collections of classroom materials and student work, and multi-disciplinary faculty discourse discussing similarities and differences of statistics across disciplines. By cross-validating the data sources, the project will address the research questions: (1) What are common learning objectives, outcomes, and experiences in introductory and advanced statistics courses across disciplines and how do these align with ASA guidelines? and (2) To what extent does faculty teaching and student work demonstrate engagement with the three themes of real data, technology, and communication across disciplines?